Learning Score Systems for Patient Mortality Prediction in Intensive Care Units via Orthogonal Matching Pursuit

The problem of predicting outcome of patients in intensive care units (ICUs) is of great importance in critical care medicine, and has wide implications for quality control in ICUs. A dominant approach to this problem has been to use an ICU score system such as, for example, the Acute Physiology and Chronic Health Evaluation (APACHE) system, and the Simplified Acute Physiology Score (SAPS) system, to compute a certain severity score for a patient from a set of clinical observations, and apply a logistic regression model on this score to obtain an estimate of the probability of mortality for the patient; owing to their simplicity, these methods are widely used by clinicians. However, existing ICU score systems are built from a fixed set of patient data, and often perform poorly when applied to a patient population with different characteristics; also, with changes in patient characteristics, a score system built from a given patient data set becomes suboptimal over time. Moreover, most of these score systems are built using semi-automated procedures that require some amount of manual intervention, making it difficult to adapt them to a new patient population. Thus there is a huge need for adaptive methods that can automatically learn predictive models from a given set of patient data, tailored to perform well on similar patient populations. Indeed, there has been much work in recent years on applying various machine learning methods to this problem; however these methods learn different representations from the score systems preferred by clinicians. In this work, we develop a machine learning method based on orthogonal matching pursuit that automatically learns a score system type model, which enjoys the benefits of both worlds: like other machine learning methods, it is adaptive; like standard score systems, it uses a representation that is easy for clinicians to understand. Experiments on real-world patient data sets show that our method outperforms standard ICU score systems, and performs at least as well as other machine learning methods that employ more complex representations. As an added advantage of using the OMP approach, one can use a group-sparse variant of OMP which allows learning models with similar performance using a smaller number of clinical observations; we include experiments with this as well.